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Do unsupervised methods for learning rich, contextualized tokenrepresentations obviate the need for explicit modeling of linguistic structurein neural network models for semantic role labeling (SRL)? We address thisquestion by incorporating the massively successful ELMo embeddings (Peters etal., 2018) into LISA (Strubell et al., 2018), a strong, linguistically-informedneural network architecture for SRL. In experiments on the CoNLL-2005 sharedtask we find that though ELMo out-performs typical word embeddings, beginningto close the gap in F1 between LISA with predicted and gold syntactic parses,syntactically-informed models still out-perform syntax-free models when bothuse ELMo, especially on out-of-domain data. Our results suggest that linguisticstructures are indeed still relevant in this golden age of deep learning forNLP.

Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL?